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- W2994176923 abstract "In this paper, we propose multi-timescale, sequential algorithms for deterministic optimization which can find high-quality solutions. The algorithms fundamentally track the well-known derivative-free model-based search methods in an efficient and resourceful manner with additional heuristics to accelerate the scheme. Our approaches exhibit competitive performance on a selected few global optimization benchmark problems." @default.
- W2994176923 created "2019-12-13" @default.
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- W2994176923 date "2019-09-01" @default.
- W2994176923 modified "2023-09-26" @default.
- W2994176923 title "Stochastic Approximation Trackers for Model-Based Search" @default.
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- W2994176923 doi "https://doi.org/10.1109/allerton.2019.8919816" @default.
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